Machine learning aided parameter analysis in Perovskite X-ray Detector

TL;DR

This paper uses machine learning to analyze how intrinsic properties of perovskite materials influence X-ray detector performance, revealing key factors like band gap and lattice parameters, and validating models with experimental data.

Contribution

It introduces a machine learning approach to link material properties with device performance in perovskite X-ray detectors, providing new insights into material-performance relationships.

Findings

Band gap influenced by atomic number of B-site metal

Lattice length parameter b impacts carrier mobility-lifetime product

Experimental validation confirms ML model accuracy

Abstract

Many factors in perovskite X-ray detectors, such as crystal lattice and carrier dynamics, determine the final device performance (e.g., sensitivity and detection limit). However, the relationship between these factors remains unknown due to the complexity of the material. In this study, we employ machine learning to reveal the relationship between 15 intrinsic properties of halide perovskite materials and their device performance. We construct a database of X-ray detectors for the training of machine learning. The results show that the band gap is mainly influenced by the atomic number of the B-site metal, and the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product ({\mu}{\tau}). An X-ray detector (m-F-PEA)2PbI4 were generated in the experiment and it further verified the accuracy of our ML models. We suggest further study on random forest regression for X-ray detector applications.